generalization algorithms

Generalization algorithms are methods used in machine learning to enable models to perform well on new, unseen data. They help the model learn patterns from training data without memorizing specifics, so it can make accurate predictions in different situations. Think of it like learning to recognize different types of apples—you understand the general concept, so you can identify new apples you've never seen before. These algorithms balance fitting the training data with maintaining flexibility, ensuring the model doesn’t overfit (memorize) or underfit (fail to learn enough). This way, the model remains reliable and effective in real-world applications.